When comparing the five models in terms of predictive performance, we found that the model containing PCs from all functional subnetworks (Model 2) gave the highest AUC of 0.775. The second best model was the L2 penalized version of this model (Model 3), which obtained an AUC of 0.741. Notably, the AUC of these two models is much higher than the PCs derived from the whole brain (Model 1, AUC = 0.505). This result suggests that the variability that best differentiates patients with schizophrenia from healthy controls is localized to functional subnetworks rather than to the whole brain. This finding was reinforced with the results of significant effects in the hand and salience subnetworks previously discussed. In comparing our classification results with other studies on this data set, we found that both Model 2 and Model 3 are competitive with state-of-the-art predictive models [1]}, [2]}, [3]}. This is particularly notable because the PCs themselves were identified in a fully unsupervised fashion, unlike the top performing classification results obtained in [1]} (accuracy \(> 92\%\)  on the COBRE data set).
